Preserving Privacy in On-Line Analytical Processing (OLAP)
Author:   Lingyu Wang ,  Sushil Jajodia ,  Duminda Wijesekera
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of hardcover 1st ed. 2007
Volume:   29
ISBN:  

9781441942784


Pages:   180
Publication Date:   19 November 2010
Format:   Paperback
Availability:   Out of print, replaced by POD   Availability explained
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Preserving Privacy in On-Line Analytical Processing (OLAP)


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Overview

Preserving Privacy for On-Line Analytical Processing addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. OLAP systems usually need to meet two conflicting goals. First, the sensitive data stored in underlying data warehouses must be kept secret. Second, analytical queries about the data must be allowed for decision support purposes. The main challenge is that sensitive data can be inferred from answers to seemingly innocent aggregations of the data. This volume reviews a series of methods that can precisely answer data cube-style OLAP, regarding sensitive data while provably preventing adversaries from inferring data. Preserving Privacy for On-Line Analytical Processing is appropriate for practitioners in industry as well as graduate-level students in computer science and engineering.  

Full Product Details

Author:   Lingyu Wang ,  Sushil Jajodia ,  Duminda Wijesekera
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of hardcover 1st ed. 2007
Volume:   29
Dimensions:   Width: 15.50cm , Height: 1.00cm , Length: 23.50cm
Weight:   0.454kg
ISBN:  

9781441942784


ISBN 10:   1441942785
Pages:   180
Publication Date:   19 November 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of print, replaced by POD   Availability explained
We will order this item for you from a manufatured on demand supplier.

Table of Contents

OLAP and Data Cubes.- Inference Control in Statistical Databases.- Inferences in Data Cubes.- Cardinality-based Inference Control.- Parity-based Inference Control for Range Queries.- Lattice-based Inference Control in Data Cubes.- Query-driven Inference Control in Data Cubes.- Conclusion and Future Direction.

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